Autonomous Mobile Robots: Planning, Navigation and Simulation

  • 21h 42m
  • Rahul Kala
  • Elsevier Science and Technology Books, Inc.
  • 2023

Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice.

Finally, the book presents fusion mechanisms to design hybrid algorithms.

  • Presents intuitive and practical coverage of all sub-problems of mobile robotics to enable easy comprehension of sophisticated modern-day robots
  • Covers a wide variety of motion planning algorithms, giving a near-exhaustive treatment of the domain with thought provoking comparisons between algorithms
  • Dives into detailed discussions on robot operating systems and other simulators to get hands-on knowledge without the need of in-house robots

About the Author

Rahul Kala is an assistant professor at the Centre of Intelligent Robotics, Indian Institute of Information Technology, Allahabad, India, where he received his B.Tech. and M.Tech. degrees in information technology. He received his Ph.D. degree in cybernetics from the University of Reading, UK in 2013. Dr. Kala has authored four books, 100 scientific papers, and is an active reviewer of leading journals of the domain. He has received numerous scholarships and grants from the Government of India, and is a recipient of the Best PhD Dissertation award from the IEEE Intelligent Transportation Systems Society.

In this Book

  • An Introduction to Robotics
  • Localization and Mapping
  • Visual SLAM, Planning, and Control
  • Intelligent Graph Search Basics
  • Graph Search-Based Motion Planning
  • Configuration Space and Collision Checking
  • Roadmap and Cell Decomposition-Based Motion Planning
  • Probabilistic Roadmap
  • Rapidly-Exploring Random Trees
  • Artificial Potential Field
  • Geometric and Fuzzy Logic-Based Motion Planning
  • An Introduction to Machine Learning and Deep Learning
  • Learning from Demonstrations for Robotics
  • Motion Planning Using Reinforcement Learning
  • An Introduction to Evolutionary Computation
  • Evolutionary Robot Motion Planning
  • Hybrid Planning Techniques
  • Multi-Robot Motion Planning
  • Task Planning Approaches
  • Swarm and Evolutionary Robotics
  • Simulation Systems and Case Studies
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